Marta Teijeira

Cytotoxicity of selected imidazolium-derived ionic liquids in the human Caco-2 cell line. Sub-structural toxicological interpretation through a QSAR study

Room-temperature ionic liquids (ILs) are considered green chemicals that may replace volatile organic solvents currently used by industry. However, toxicological effects of ILs are not well known. In this study, we describe the cytotoxicity of selected imidazolium-derived ILs in Caco-2 cells, prototypical human epithelial cells. The most toxic IL was 1-decyl-3-methylimidazolium chloride ([C10mim][Cl]), whereas the least toxic was 1,3-dimethylimidazolium methyl sulfate ([C1mim][MSO4]). Using the toxicological experimental data obtained we developed a Quantitative Structure–Activity Relationship (QSAR) study using the Topological Sub-Structural Molecular Design (TOPS-MODE) approach. The model found showed excellent statistical parameters and from their interpretation, we arrived to some important conclusions such as: For 1-alkyl-3-methylimidazolium chloride derivatives, a correlation between the R2 alkyl chain length and toxicity was observable. A positive contribution of p-chloro substituent in benzyl ring is detected among 1-methylarylimidazolium chloride derivatives. Regarding the contribution of the anion, anion chloride presents a positive contribution to toxicity whereas for compounds [C1mim][MSO4] and [C1eim][ESO4], a negative fragment contribution can be detected in anion methyl or ethyl sulfate. The EC50 values determined for the ILs analysed in this study in Caco-2 cells are lower than the data reported in the literature on the toxicity of classical solvents assessed with cell lines. In conclusion, our results indicate that the possible cytotoxic effect of ILs should be considered in the design and overall evaluation of these compounds. Nevertheless, any toxicity evaluation of ILs should take their bioavailability into account, which will be affected by their low volatility, compared to conventional solvents.

Variable Selection Methods in QSAR: An Overview

Variable selection is a procedure used to select the most important features to obtain as much information as possible from a reduced amount of features. The selection stage is crucial. The subsequent design of a quantitative structure-activity relationship (QSAR) model (regression or discriminant) would lead to poor performance if little significant features are selected. In drug design modern era, by the means of combinatorial chemistry and high throughput screening, an unprecedented amount of experimental information has been generated. In addition, many molecular descriptors have been defined in the last two decays. All this information can be analyzed by QSAR techniques using adequate statistical procedures. These techniques and procedures should be fast, automated, and applicable to large data sets of structurally diverse compounds. For that reason, the identification of the best one seems to be a very difficult task in view of the large variable selection techniques existing nowadays. The intention of this review is to summarize some of the present knowledge concerning to variable selection methods applied to some well-known statistical techniques such as linear regression, PLS, kNN, Artificial Neural Networks, etc, with the aim to disseminate the advances of this important stage of the QSAR building model.

AM1 theoretical study, synthesis and biological evaluation of some benzofuran analogues of anti-inflammatory arylalkanoic acids

Using the semi-empirical quantum-mechanical method AM1, the molecular geometries of the arylalkanoic acids, indomethacin, naproxen and ibuprofen, were optimized and their frontier orbital charge distributions evaluated. Then, these molecular parameters were compared in order to identify structure-activity relationships and, on the basis of these, four benzofuran-3-acetic acids were designed as potential non-steroidal anti-inflammatory agents, and rapidly synthesized by a novel and easily generalized route. Notwithstanding the structural similarities between the synthesized compounds and the anti-inflammatory arylalkanoic acids, these compounds did not appreciably inhibit human platelet cyclooxygenase in vitro.

Synthesis and structure–activity relationships of new arylpiperazines: para substitution with electron-withdrawing groups decrease binding to 5-HT1A and D2A receptors

Abstract Compounds in which N -phenylpiperazines were linked by a propyloxy chain to position 6 or 7 of a coumarin ring were designed and synthesised, and their affinities for 5-HT 1A and D 2A receptors were determined by radioligand binding assays. The influence of para substitution in the phenyl ring, substitution at position 4 of the coumarin system, and the coumarin position at which the piperazinylalkyl chain is linked was explored. Electron-withdrawing phenyl ring substituents para to the piperazine strongly reduced activity at both receptors. Binding at 5HT 1A was influenced by the bulk of substituents at position 4 of the coumarin system, and binding at D 2A by their electronic properties. Neither binding affinity was significantly affected by whether the piperazinylalkyl chain was inserted at position 6 or 7 of the coumarin system.

Quantitative structure activity relationships as useful tools for the design of new adenosine receptor ligands. 1. Agonist

In order to minimize expensive drug failures it is essential to determine the potential biological activity of new candidates as early as possible. In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of a drugs biological activity is advisable even before synthesis and this can be achieved using predictive biological activity methods. In this sense, computer aided rational drug design strategies like Quantitative Structure Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of traditional QSAR applications in the development of new agonist molecules with affinity toward adenosine receptors is scarce. This review attempts to summarize the current level of knowledge concerning computational affinity predictions for adenosine receptors using QSAR models based on knowledge of the agonist ligands. Several computational protocols and different 2D and 3D descriptors have been described in the literature for these targets, but more effort is still required in this area.

Multidimensional Drug Design: Simultaneous Analysis of Binding and Relative Efficacy Profiles of N6-substituted-4¢-thioadenosines A3 Adenosine Receptor Agonists

Desirability theory (DT) is a well‐known multi‐criteria decision‐making approach. In this work, DT is employed as a prediction model (PM) interpretation tool to extract useful information on the desired trade‐offs between binding and relative efficacy of N6‐substituted‐4′‐thioadenosines A3 adenosine receptor (A3AR) agonists. At the same time, it was shown the usefulness of a parallel but independent approach providing a feedback on the reliability of the combination of properties predicted as a unique desirability value. The appliance of belief theory allowed the quantification of the reliability of the predicted desirability of a compound according to two inverse and independent but complementary prediction approaches. This information is proven to be useful as a ranking criterion in a ligand‐based virtual screening study. The development of a linear PM of the A3AR agonists overall desirability allows finding significant clues based on simple molecular descriptors. The model suggests a relevant role of the type of substituent on the N6 position of the adenine ring that in general contribute to reduce the flexibility and hydrophobicity of the lead compound. The mapping of the desirability function derived of the PM offers specific information such as the shape and optimal size of the N6 substituent. The model herein developed allows a simultaneous analysis of both binding and relative efficacy profiles of A3AR agonists. The information retrieved guides the theoretical design and assembling of a combinatorial library suitable for filtering new N6‐substituted‐4′‐thioadenosines A3AR agonist candidates with simultaneously improved binding and relative efficacy profiles. The utility of the desirability/belief‐based proposed virtual screening strategy was deduced from our training set. Based on the overall results, it is possible to assert that the combined use of desirability and belief theories in computational medicinal chemistry research can aid the discovery of A3AR agonist candidates with favorable balance between binding and relative efficacy profiles.

Synthesis of Cis-1-[(2-Hydroxymethyl) Cyclopentyl]Uridine and Determination of its Conformation by X-Ray Crystallography and Ami Theoretical Calculations

Abstract Abtract:The title compound 1 was prepared from racemic cis-2-hydroxymethylcyclo pentylamine, constructing the heterocyclic base in a two-step procedure (64 % yield). The crystal structure of the compound was determined and its conformation was studied using AMI theoretical calculations. The calculated low-energy conformations were similar to those reported for several other nucleoside analogues, and one of them closely corresponded to the X-ray structure.

Classifier ensemble based on feature selection and diversity measures for predicting the affinity of A(2B) adenosine receptor antagonists.

A(2B) adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A(2B) adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A(2B) adenosine receptor antagonists, and it can be used to develop other QSAR models.

Ligand-Based Virtual Screening Using Tailored Ensembles: A Prioritization Tool for Dual A 2A Adenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors

BACKGROUND Virtual Screening methodologies have emerged as efficient alternatives for the discovery of new drug candidates. At the same time, ensemble methods are nowadays frequently used to overcome the limitations of employing a single model in ligand-based drug design. However, many applications of ensemble methods to this area do not consider important aspects related to both virtual screening and the modeling process. During the application of ensemble methods to virtual screening the proper validation of the models in virtual screening conditions is often neglected. No analysis of the diversity of the ensemble members is performed frequently or no considerations regarding the applicability domain of the base models are being made. METHODS In this research, we review basic concepts and definitions related to virtual screening. We comment recent applications of ensemble methods to ligand-based virtual screening and highlight their advantages and limitations. RESULTS Next, we propose a method based on genetic algorithms optimization for the generation of virtual screening tailored ensembles which address the previously identified problems in the current applications of ensemble methods to virtual screening. CONCLUSION Finally, the proposed methodology is successfully applied to the generation of ensemble models for the ligand-based virtual screening of dual target A2A adenosine receptor antagonists and MAO-B inhibitors as potential Parkinsons disease therapeutics.

Synthesis and cytostatic activity of purine nucleosides derivatives of allofuranose

Several new purine nucleosides derivatives of allofuranose were prepared according to Vorbrüggen method, starting from 1,2,5,6-di-O-isopropylidene-α-D-allofuranose and using 1,2,3,5,6-pentaacetoxy-β-D-allofuranose as key intermediate. The synthesized allofuranosyl nucleosides, as well as some acetyl derivatives, were evaluated for their cytotoxicity in vitro in three human cancer cell lines (MCF-7, Hela-229 and HL-60). Among the studied compounds the 9-(2,3,5,6-tetra-O-acetyl-β-D-allofuranosyl)-2,6-dichloropurine (9) was the most potent one on the three cell lines evaluated, being its activity against HL-60 cells similar to cisplatin.